Seasonal tropical cyclone forecasts - CSU Tropical Weather ...

[Pages:13]Seasonal tropical cyclone forecasts

by Suzana J. Camargo?, Anthony G. Barnston1, Philip J. Klotzbach2 and Christopher W. Landsea3

Introduction

Statistical and dynamical seasonal tropical cyclone

Seasonal forecasts of tropical activity forecasts are proposed to be made available

cyclone activity in various regions have been developed since the first

on a public Website for forecasters and other users.

attempts in the early 1980s by Neville

Nicholls (1979) for the Australian

region and William Gray (1984(a), (b))

for the North Atlantic region. Over et al., 2006). These quadrennial seasonal tropical cyclone forecasts

time, forecasts for different regions, workshops, co-sponsored by the has increased tremendously since

using differing methodologies, have WMO Commission for Atmospheric they were first produced, especially

been developed. Tourism in various S c i e n c e Tr o p i c a l M e t e or o l o g y after 2004, when 10 tropical cyclones

regions, such as the US Gulf and Research Programme and the struck Japan and four hurricanes

East Coasts and the Caribbean, World Weather Watch Tropic al impacted Florida, USA.

is impac ted by these seasonal Cyclone Programme, bring together

forecasts. Insurance and re-insurance tropical cyclone forecasters and Although landfall forec as t s are

companies also make use of seasonal researchers to review progress and par ticularly impor tant to users,

forecasts in their policy decisions. plan for future activities in topics landfall forecast skill is still limited.

It is fundamental to provide these such as seasonal forecasts. During As seasonal tropical cyclone forecasts

users with information about the IWTC-VI, forecasters from various improve, more attention will be given

accuracy of seasonal forecasts. countries shared information about to particular details such as regional

Seasonal forecasts have limited use seasonal tropical cyclone forecasts landfall probabilities. The use of

for emergency managers, because of currently being issued by their such specific forecasts will become

the lack of skill in predicting impacts respective countries--which was more widespread and significant to

at the city or county level.

often information not well known decision-makers and residents in

by other scientists present.

coastal areas.

As has been the case in some of

the previous WMO International Forecasters in National Meteorological With the popularization of these

Workshops on Tropical Cyclones and H ydrologic al Ser vic e s are forecasts, it is fundamental that

(IT WC), a review of the progress interested in seasonal forecasts their documentation and verification

on seasonal forecasts of tropical because they are frequently asked become widely available. It is

cyclone activity was presented at questions by the media and various recommended that WMO develop

the IW TC-VI in San Jos?, Costa decision-makers. Interest from the guidelines for the development

Rica, in November 2006 (Camargo media and the general public in and validation of these forecasts,

similar to the protocol that has been

developed for global seasonal climate

1 International Research Institute for Climate and Society, The Earth Institute at Columbia University, Palisades, New York, USA

2 Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado, USA

3 NOAA National Hurricane Center, Miami, Florida, USA

(temperature and precipitation) forecasts (WMO, 2001). A summary of grops that issue tropical cyclone seasonal forecasts is given in

Table I.

WMO Bulletin 56 (4) - October 2007 | 297

Table I -- Seasonal tropical cyclone forecasts: groups that issue the forecasts, regions in which the forecasts are issued, forecast type, Website where the forecast is available.

Group

Basins

City University of Hong Kong, Western North Pacific China (CityU)

Colorado State University, USA (CSU)

Atlantic

Cuban Meteorological Institute (INSMET)

Atlantic

European Centre for MediumRange Weather Forecasts (ECMWF)

Atlantic Australian Eastern North Pacific North Indian South Indian South Pacific Western North Pacific

International Research Institute for Climate and Society (IRI)

Atlantic Australia Eastern North Pacific South Pacific Western North Pacific

Macquarie University, Australia

Australia / southwest Pacific

Meteorological Office, United North Atlantic Kingdom (MetOffice)

National Meteorological Service, Mexico (NSM)

Eastern North Pacific

National Climate Centre, China

Western North Pacific

NOAA hurricane outlooks

Atlantic Eastern North Pacific Central North Pacific

Tropical Storm Risk (TSR)

Atlantic Western North Pacific Australian region

Type Statistical Statistical Statistical Dynamical

Dynamical

Statistical Dynamical Statistical Statistical Statistical Statistical

Website (collaborating agencies only)

ht tp : / / iri.columbia.edu / forecas t / tc_fcs t /

ht tp : / / w w w.metof .uk / weather/ tropicalcyclone/northatlantic http:// prh.hnl/cphc

Statistical seasonal hurricane forecasts

Colorado State University

Initial seasonal predictions for the North Atlantic basin (Gray, 1984(a), (b)) were issued by Colorado State University in early June and early August, beginning in 1984, using statistical relationships between tropical cyclone activity and El Ni?o/ Southern Oscillation (ENSO), the Quasi-Biennial Oscillation (QBO) and Caribbean basin sea-level pressures. Comparatively, more tropical cyclones were predicted in the cool phase

of ENSO, when the QBO was in its west phase and Caribbean basin sealevel pressures were below normal. Statistical forecast techniques for North Atlantic tropical cyclones have evolved since these early forecasts. Additional predictors were added to the original forecast scheme, the QBO is not used as a predictor anymore and the seasonal forecasts started being issued in early December of the previous year. Klotzbach and Gray (2004) and Klotzbach (2007) explain the current forecast scheme.

Owens and Landsea (2003) examined the skill of Gray's operational

Atlantic seasonal tropical cyclone forecasts relative to climatology and persistence. Their analysis indicated that for the analysed period (1984? 2001), both the basic statistical forecasts and an adjusted version demonstrated skill over climatology and persistence, with the adjusted forecasts being more skilful than the basic forecasts.

Figure 1 shows the skill of the CSU forecasts for various leads, using linear correlation as a skill measure. The skill improves tremendously in June and August, probably because the ENSO spring barrier is over. Since the ENSO

298 | WMO Bulletin 56 (4) - October 2007

Correlation

1

NS

NSD

0.8

H

HD

0.6

IH

IHD

NTC

0.4

0.2

0

-0.2

the number of tropical cyclones in the Central North Pacific region based on the ENSO state and the Pacific decadal oscillation.

Tropical Storm Risk (TSR)

Tropical Storm Risk issues statistical forecasts for tropical cyclone activity in the Atlantic, western North Pacific and Australian regions. The seasonal prediction model uses ENSO forecasts (Lloyd-Hughes et al., 2004) to predict the western North Pacific ACE index and is skilful in hindcast mode in that region (Lea and Saunders, 2006).

-0.4 December

April

June

Month

August

Figure 1 -- Correlations of the CSU seasonal forecasts for different leads: December (1992? 2006), April (1995-2006), June (1984-2006 or 1990-2006) and August (1984-2006 or 1990-2006). The correlations are given for: number of named storms (NS), number of named storm days (NSD), number of hurricanes (H), number of hurricane days (HD), number of intense hurricanes (IH), number of intense hurricane days (IHD) and net tropical cyclone activity (NTC). Significant correlations at the 95% significance level are: June ? NS, NSD, H, HD, IHD, NTC, August ? NS, NSD, H, HD, IH and NTC. None of the correlations is significant for the December and April leads.

state is usually defined by June, the hurricane forecasts made in June or later become more skilful. Another reason for a higher skill in June and August is that the season is about to start or has already started.

CSU started issuing forecasts of landfall probabilities in August 1998. The landfall probabilities are based upon a forecast of net tropical cyclone activity. In general, when an active season is predicted (high net tropical cyclone activity), the probability of landfall is increased (Klotzbach, 2007).

National Oceanic and Atmospheric Administration (NOAA)

NOAA has been issuing seasonal hurricane outlooks for the Atlantic and the eastern North Pacific regions since 1998 and 2003, respectively. These outlooks are provided to the public

as deterministic and probabilistic, using terciles. They are based on the state of ENSO (Gray, 1984(a)) and the tropical multi-decadal mode (e.g. Chelliah and Bell, 2004), which incorporates the leading modes of tropical convective rainfall variability occurring on multi-decadal time scales. Important aspects of this signal that are related to an active Atlantic hurricane season include a strong West African monsoon, reduced vertical wind shear in the tropical Atlantic, suppressed convection in the Amazon basin and high tropical Atlantic sea-surface temperatures (SSTs) (Goldenberg et al., 2001). The NOAA forecasts and verifications for named storms, hurricanes, major hurricanes and accumulated cyclone energy (ACE) (Bell et al., 2000) over the period from 1998?2006 are given in Figure 2.

Sinc e 19 97, the Central Pacif ic Hurricane Center issues in May seasonal forecasts for the range of

In a recent paper (Saunders and Lea, 2005), TSR describes its new forecast model, issued in early August, for seasonal predictions of hurricane landfall activity for the US coastline. The model uses July wind patterns to predict the seasonal US ACE index (effectively, the cumulative wind energy from all tropical cyclones which strike the USA). The July heightaveraged winds in these regions are indicative of atmospheric circulation patterns that either favour or hinder hurricanes from reaching US shores. The model correctly anticipates whether US hurricane losses are above- or below-median in 74 per cent of the hindcasts for the 1950?2003 period. The model also performed well in "real-time" operation in 2004 and 2005, while over-predicting in 2006.

City University of Hong Kong, China

Johnny Chan and colleagues have issued seasonal tropical cyclone forecasts for the North-west Pacific basin (number of tropical cyclones and typhoons) since 1997. The statistical predictions are based on various environmental conditions in the prior year, up to the northern hemisphere spring of the forecast season. The most prominent atmospheric and oceanic conditions include ENSO, the extent of the Pacific subtropical ridge and the intensity of the India-

WMO Bulletin 56 (4) - October 2007 | 299

Number of troprical storms

NOAA tropical storms forecasts

Observations

May forecasts

26

August forecasts

Climatological mean

22

18

14

10

NOAA hurricanes forecasts

18 Observations

May forecasts

15

August forecasts Climatological mean

12

9

6

Number of hurricanes

parameters (see Table II) (Ballester et al., 2004(a) and (b)). The Cuban Meteorological Institute also issues statistical landfall forecasts for Cuba based on a discriminant function methodology (Davis, 1986).

Florida State University (FSU)

6 1998 2000 2002 2004 2006 Year

NOAA major hurricanes forecasts

8

Observations

May forecasts

August forecasts

6

Climatological mean

4

3 1998 2000 2002 2004 2006 Year

360 300 240

NOAA ACE forecasts

Observations May forecasts August forecasts Climatological mean

180

James Elsner and colleagues have been developing techniques for modelling seasonal hurricane activity and landfall. Although their forecasts are not produced operationally, their methodology is currently used to issue region-specific forecasts for various companies (James Elsner,

Percentage of median ACE

Number of major hurricanes

120 2

60

personal communication, 2006). The FSU group pioneered various topics in seasonal forecasting, such as the use

0 1998 2000 2002 2004 2006 Year

0 1998 2000 2002 2004 2006

Year

of a Poisson distribution for hurricane counts (Elsner and Schmertmann, 1993), the influence of the phase

of the North Atlantic Oscillation on

Figure 2 -- NOAA forecasts (May and August leads) and observations for tropical

Atlantic hurricane tracks and US

cyclones with tropical storm intensity or higher, hurricanes, major hurricanes and ACE coastal hurricane activity (Elsner et

(Accumulated Cyclone Energy, Bell et al., 2000) for the period 1998-2006

al., 2001), and the development of a

skilful statistical model for seasonal

forecasts of landfall probability over

Burma trough (Chan et al., 1998). For

a few years, forecasts of the number

of tropical cyclones making landfall

40

were also issued (Liu and Chan,

City University of Hong Kong June forecasts

TS+TY forecasts

2003). Currently, the landfall forecast

scheme for the South China Sea is

35

being improved. The City University

TS+TY observations TS+TY forecasts TS+TY forecasts

of Hong Kong, China, forecasts

and the verifications are shown in

30

Figure 3. In most years, the observed

number of tropical cyclones is within

25

the range of the forecast number of

tropical cyclones, with the exception

of 2006.

20

Number of tropical cyclones

Cuban Meteorological

15

Institute

The Cuban Meteorological Institute has been issuing seasonal forecasts of Atlantic hurricane activity since 1996. Currently, the Cuban seasonal forecast is based on the solution of a regression and an analogue method and predicts various tropical cyclone

1999

2000

2001

2002

2003 Year

2004

2005

2006

2007

Figure 3 -- Verification of the City University of Hong Kong, China, forecasts issued in early June: (top) the number of tropical storms and typhoons (TS+TY) observed and forecast range; (bottom) the number of typhoons (TY) observed and forecast range. In green are the mean climatological number and the corresponding climatological standard deviation.

300 | WMO Bulletin 56 (4) - October 2007

Table II -- Seasonal tropical cyclone forecasts: predictors and outputs used for each group. The group acronyms are defined in Table I. Other acronyms: TCs (tropical cyclones), ENSO (El Ni?o-Southern Oscillation) , SST (sea-surface temperature), SLP (sealevel pressure), SOI (Southern Oscillation Index), OLR (outgoing long-wave radiation) and MDR (main development region).

Group

Predictors

Outputs

CityU CSU

INSMET

ECMWF IRI

1. ENSO 2. Extent of the Pacifc subtropical ridge 3. Intensity of India-Burma trough

1. Number of TCs 2. Number of named TCs 3. Number of typhoons

1. SST North Atlantic 2. SST South Atlantic 3. SLP South Pacific 4. ENSO 5. Atlantic meriodinal Mode

1. Number of named TCs 2. Named of named TC days 3. Number of hurricanes 4. Number of hurricane days 5. Number of major hurricanes 6. Named of major hurricane days 7. Accumulated cyclone energy 8. Net tropical cyclone energy

1. North Atlantic winds 2. ENSO 3. Intensity of the Atlantic subtropical ridge 4. SST North Atlantic 5. Quasi Biennial Oscillation

1. Number of named TCs 2. Number of hurricanes 3. Number of named TCs in the Atlantic MDR, Caribbean and

Gulf of Mexico (separately) 4. First day with TC genesis in the season 5. Last day with a TC active in the season 6. Number of named TCs that form in the Atlantic MDR and

impact the Caribbean

1. Coupled dynamical model 2. Model TCs identified and tracked

1. Number of named TCs 2. Mean location of TC genesis

1. Various SST forecast scenarios. 2. Atmospheric models 3. Model TCs identified and tracked.

1. Number of named TCs 2. Accumulated cyclone energy (northern hemisphere only) 3. Mean location of TCs (western North Pacific only)

Macquarie U. Met Office

1. SOI index

1. Number of TCs

2. Equivalent potential temperature gradient 2. Number of TCs in the Coral Sea

1. Coupled dynamical model 2. Model TCs identified and tracked

1. Number of named TCs

SMN

1. SST anomalies 2. Equatorial wind anomalies 3. Equatorial Pacific OLR

NOAA (Atlantic and Eastern Pacific)

1. ENSO 2. Tropical multi-decadal mode 3. Atlantic SST

NOAA (Central Pacific)

1. ENSO 2. Pacific Decadal Oscillation

1. Number of TCs 2. Number of tropical storms 3. Number of hurricanes 4. Number of major hurricanes

1. Number of named TCs 2. Number of hurricanes 3. Number of major hurricanes 4. Accumulated cyclone energy

1. Number of TCs

Tropical Storm Risk (TSR)

1. Trade winds 2. MDR SST 3. ENSO 4. Sea-level pressure central Northern

Pacific

1. Number of named TCs 2. Number of hurricanes 3. Number of major hurricanes 4. Accumulated Cyclone Energy 5. ACE landfalling TCs 6. Number of landfalling named TCs 7. Number of landfalling hurricanes 8. Number of landfalling major hurricanes

WMO Bulletin 56 (4) - October 2007 | 301

the south-eastern USA (Lehmiller et al., 1997). More recently, Elsner and Jagger (2006) built a Bayesian model for seasonal landfall over the USA, using as predictors May-June values of the North Atlantic Oscillation; the Southern Oscillation Index; and the Atlantic Multi-decadal Oscillation.

National Meteorological Service of Mexico

The National Meteorological Service of Mexico has produced a seasonal tropical cyclone activity forecast for the North-east Pacific basin since 2001. Their methodology uses analogue years and was originally developed by Arthur Douglas at Creighton University. The forecasts are first issued in January and updated in May, June and August. Various predictors are used, including SSTs and atmospheric circulation patterns over the North Pacific and outgoing long-wave radiation over the equatorial Pacific. A cluster analysis is then used to identify the most similar years in the historical record.

Australia/SouthWest Pacific

Forecasts for the Australian/SouthWest Pacific region are presented annually in the December issue of the Experimental Long-Lead Forecast Bulletin since the 2004?2005 season. These forecasts are based on a Poisson regression model and use as predictors the September saturated equivalent potential temperature gradient and the Southern Oscillation Index (McDonnell and Holbrook, 2004(a), (b)). They also developed forecasts for smaller subregions, among which the highest hindcast skill is in the Coral Sea, where ENSO has its strongest influence.

Other forecasts

The China Meteorological Administration has been issuing forecasts of typhoon activity for the

western North Pacific since the early 1980s. Since 1995, when the National Climate Centre was established, a nationwide workshop has been held in April. Forecasts for landfalling typhoons in the South China Sea and eastern China have also been developed. These seasonal forecasts are being continuously improved by the National Climate Centre and the Shanghai Typhoon Institute.

The North Carolina State University forecast group presented a new seasonal forecast methodology for Atlantic hurricanes at the 27th Conference on Hurricanes and Tropical Meteorology of the American Meteorological Society (T. Yan et al., 2006) and gave their forecast for the 2006 season. These forecasts for number of hurricanes and number of landfalling hurricanes are based on ENSO, vertical wind shear, the Atlantic dipole mode and the North Atlantic Oscillation, as discussed in Xie et al. (2004, 2005).

It is likely that other statistical forecasts are being issued by various agencies around the world of which we are not aware.

Dynamical tropical cyclone seasonal forecasts

Many studies have shown that lowresolution climate models are able to simulate tropical cylone-like disturbances (e.g. Manabe et al., 1970; Bengtsson et al., 1982). These disturbances have properties similar to those of observed tropical cyclones but are typically weaker and larger in scale. They are more realistic in higher-resolution simulations (e.g. Bengtsson et al., 1995).

While low-resolution simulations are not adequate for forecasting individual cyclone tracks and intensities, some climate models have skill in forecasting levels of seasonal tropical cyclone activity. They are able to reproduce typical ENSO influences (e.g. Vitart et al., 1997).

The International Research Institute (IRI) for Climate and Society, the European Centre for Medium-range Weather Forecasts (ECMWF) and more recently the UK Met Office issue experimental seasonal forecasts of tropical storm frequency based on dynamical models. The IRI and Met Office forecasts are freely available on the Web. The ECMWF forecasts are available online to collaborating agencies. The ECMWF and Met Office forecasts are based on coupled oceanatmosphere models (Vitart and Stockdale, 2001). The experimental IRI forecasts are obtained using a twotier procedure. First, various possible scenarios for SSTs are predicted, using statistical or dynamical models. Then, atmospheric models are forced with those predicted SSTs. In both cases, the tropical cyclone-like vortices are identified and tracked in the atmospheric model outputs (e.g. Camargo and Zebiak, 2002). The IRI also issues ACE forecasts based on dynamical models for several northern hemisphere regions. The IRI forecasts are probabilistic by tercile category (above normal, normal, below normal), as in the example for the Atlantic in 2006 (Figure 4). The rank probability skill score for the IRI July forecasts for the months of August to October in the Atlantic for the period 2003-2006 is positive with an approximate value of 0.12.

The skill of some of the best performing dynamical models in predicting the frequency of tropical storms is comparable to the skill of statistical models in some ocean basins. Over the North and South Indian Ocean, dynamical models usually perform poorly (Camargo et al., 2005). It is not clear to what extent this is due to model errors or to a lack of predictability. Similarly to the seasonal climate forecasts, combining different model forecasts (multi-model ensemble forecasts) appears to produce overall better forecasts than individual model ensemble forecasts (Vitart, 2006). The hindcast skill of various dynamical climate models in predicting seasonal

302 | WMO Bulletin 56 (4) - October 2007

Probability (percentage)

Probability forecasts for number of tropical cyclones

80

Below normal Norht Atlantic

70

Normal

ASO 2006

Above normal 60

An alternate approach for forecasting tropical cyclones using climate models involves simulating the interannual variability of environmental variables that affect tropical cyclone activity (e.g. Ryan et al., 1992). A drawback of this

50

approach is that it requires a choice

of which variables or combinations

40

of variables should be analysed.

Climatological

Recently, a few studies compared

30

probability

both approaches using the same

(33%)

climate models (e.g. McDonald et al.,

20

2005; Camargo et al., 2007(b)). Both

10

approaches may be used in the future,

since they are complementary.

0

April

May

June

July

August

Month forecast was issued

The importance of

Figure 4 -- IRI experimental dynamical forecast probabilities for the August-October

ENSO prediction

(ASO) 2006 period in the Atlantic for different lead times. The normal category is defined

as six to nine named tropical cyclones, the below-normal category as five or less named ENSO events shif t the seasonal

tropical cyclones and the above-normal category as 10 or more named tropical cyclones. te mp er a t ur e and pr e c ipi t a tion

In 2006 there were seven named tropical cyclones in the Atlantic during ASO, i.e. the

patterns in a consistent manner in

season was in the normal category.

many parts of the world (Bradley et al.,

1987; Ropelewski and Halpert, 1987).

Depending on the time of the year,

tropical cyclone activity is discussed Mozambique (Vitart et al., 2003). ENSO phenomena can be predicted

in Camargo et al. (2005) and Vitart Another possible approach to predict with modest-to-moderate skill months

(2006). The European multi-model the risk of tropical cyclone landfall in advance (Cane and Zebiak, 1985).

(EUROSIP) dynamical forecasts of using dynamical models would ENSO forecasts are routinely used as

tropical cyclone frequency skilfully incorporate statistical techniques a major component in probabilistic

distinguished the very active Atlantic such as track clustering (Camargo seasonal climate forecasts at various

hurricane season in 2005 from the et al., 2007(a)).

centres (Goddard et al., 2001).

below-average season in 2006 (Vitart et al., 2007). The EUROSIP forecasts are

Forecast

Observations

2 standard deviations

Correlation = 0.78 (1.00) RMS error = 3.07 (4.56)

not currently available to the public.

26

25

The predicted number of tropical

24 23

storms in the EUROSIP hindcasts

22 21

Number of tropical storms

20

(1993-2004) and real-time forecasts

19

18

(2005-2006) is shown in Figure 5

17 16

(Vitart et al., 2007, Figure 3).

15 14

13

12

11

Seasonal prediction of tropical cyclone

10

9

landfall represents a major challenge

8 7

for dynamical models. Tropical

6 5

cyclones take an unrealistically

4 3

2

poleward track in some of the models

1

used in seasonal forecasting systems, due partly to the coarse horizontal model resolution, which leads to

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Year

larger vortices than observed ones. Figure 5 -- Number of tropical storms from July to November predicted by the EUROSIP

These larger vortices would likely be (median) starting on 1 June (blue solid line) for the period 1993-2006. Hindcasts were

more influenced by the beta effect. used for the period 1993-2004, and real-time forecasts in 2005-2006. The observations

Finer-resolution climate models are are given in the dotted red line and the green vertical lines represent two standard

able to reproduce landfall differences deviations within the multi-model ensemble distribution. (Figure originally from Vitart

related to ENSO impacts, such as in et al., 2007)

WMO Bulletin 56 (4) - October 2007 | 303

Tropical cyclones are also affected by ENSO in various parts of the world. The relationship between them was first documented in a series of papers by Neville Nicholls for the Australian region (Nicholls, 1979). During warm ENSO events, fewer cyclones occur near Australia, while in cold events, an enhanced risk of landfall in Australia exists with more cyclones affecting Queensland. The impact of ENSO on North Atlantic cyclones was first discussed by William Gray (Gray, 1984(a)). The influence of ENSO on western North Pacific typhoon activity was first explored in Chan (1985). In all cases, the relationship of ENSO and tropical cyclones was subsequently developed into statistical forecasts predicting seasonal activity.

The state of ENSO is of fundamental importance in the seasonal activity level and character of tropical cyclones in all ocean basins. This is the case not only because of the obvious relevance of the ENSO state to the SST anomaly pattern in the tropical ocean basin but also because of the influence of ENSO on fields of local atmospheric variables, such as the large-scale horizontal pattern of anomalous circulation and geopotential height, upper-level divergence and vertical wind shear. Thus, our ability to predict ENSO state several months in advance is critical to being able to predict tropical cyclone activity in the same timeframe, using either statistical or dynamical methodologies.

ENSO predictability follows a wellknown seasonal cycle, in which the ENSO state for 4-6 months into the future is more accurately predicted from a starting time between July and November than between January and March. This is due to a "predictability barrier" that exists between April and June, such that forecasts made just before this period are hindered by the barrier. The seasonal timing of the predictability barrier is related to the life cycle of ENSO episodes, which often emerge between April and June and endure until the following March to May. Once an episode has begun,

predicting its continuation for the next 9 to 12 months is a much easier task than predicting its initial appearance. Even a strong El Ni?o, such as that of 1997/1998, was not well anticipated before signs of the initial onset were observed in the northern hemisphere spring of 1997 (Barnston et al., 1999). Even after becoming apparent in the observations in late April and May 1997, the strength of this extreme El Ni?o event was underpredicted by most models, although a few models did correctly anticipate the rapid weakening in the spring of 1998 (Landsea and Knaff, 2000).

There is varying skill in ENSO forecasts as evidenced by the Nino3

forecasts obtained with the Zebiak and Cane (1987) simple coupled model (Figure 6). While these skills are for a particular model, they roughly approximate the skills for predictions of other dynamical as well as statistical models, because they represent basic predictability that is reflected similarly across most of the present models. It is clear that predictive skill for forecasts made in March is high for only 2-3 months, while, for forecasts made in August, the skill extends to longer lead times. Improvements in predictive skill using today's more advanced dynamical models have been small and it remains to be seen whether or not substantial improvements

Jan.

Nov.

Sept.

July

May

Mar.

Jan. 0

5

10

15

20

Lead time in months

0.2

0.4

0.6

0.8

Figure 6 -- Skill of the Zebiak and Cane ENSO forecast model for prediction of Nino3 sea-surface temperature anomalies for varying hindcast start months and hindcast lead time. The colours indicate skill as a correlation between the hindcasts and the corresponding observations. The vertical axis indicates the month from which the hindcast is made and the horizontal axis is the lead time. For example, a hindcast made from July with a lead time of two months would be a hindcast for September and with a lead time of 24 months (right side of figure) would be a hindcast for the July two years after the hindcast was made.

304 | WMO Bulletin 56 (4) - October 2007

 ................
................

In order to avoid copyright disputes, this page is only a partial summary.

Google Online Preview   Download